Multi-scale Region-aware Implicit Neural Network for Medical Images Matting

Medical image segmentation is a critical task in computer-assisted diagnosis and disease monitoring, where labeling complex and ambiguous targets poses a significant challenge. Recently, the alpha matte has been investigated as a soft mask in medical scenes, using continuous values to quantify and distinguish uncertain lesions with high diagnostic values. In this work, we propose a multi-scale regions-aware implicit function network for the medical matting problem. Firstly, we design an regions-aware implicit neural function to interpolate over larger and more flexible regions, preserving important input details. Further, the method employs multi-scale feature fusion to efficiently and precisely aggregate features from different levels. Experimental results on public medical matting datasets demonstrate the effectiveness of our proposed approach, and we release the codes and models in https://github.com/xuyanyu-shh/MedicalMattingMLP.